Recently, Bayesian learning concept has been introduced to reduce the reliability performance gap between ML and linear receivers. These receivers are referred to as the Bayesian learning receivers [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. The Bayesian learning concept is used to incorporate detection probability measures when estimating the detected symbols from the received signals[7]}. The best Bayesian learning receivers in term of bit-error-rate (BER) performance combines the Bayesian learning concept with MMSE scheme, referred to as expectation propagation (EP) receivers [1]}, [2]}, [3]}, [4]}. Despite a significant performance improvement compared to the MMSE receivers, matrix inversion operations are still required. This results in a exponentially latency processing at the receivers. To avoid the matrix inversion operation, another Bayesian based receiver referred to as approximate message passing (AMP) receiver [5]}, [6]} has been proposed, albeit at the cost of higher BER as compared to EP receivers. Note also that all Bayesian M-MIMO receivers above rely on the learning parameters that needs to be searched by using a trial and error process for different environments.
